def main(): parser = argparse.ArgumentParser(description='Parse the config path') parser.add_argument( "-c", "--config", dest="path", help= 'The path to the config file. e.g. python run.py --config dc_config.json' ) config = parser.parse_args() with open(config.path) as f: args = json.load(f) args = AttrDict(args) device = torch.device(args.device) args.model = onssen.nn.chimera(args.model_options) args.model.to(device) args.train_loader = data.edinburgh_tts_dataloader(args.model_name, args.feature_options, 'train', args.cuda_option, self.device) args.valid_loader = data.edinburgh_tts_dataloader(args.model_name, args.feature_options, 'validation', args.cuda_option, self.device) args.optimizer = utils.build_optimizer(args.model.parameters(), args.optimizer_options) args.loss_fn = loss.loss_chimera_psa trainer = onssen.utils.trainer(args) trainer.run() tester = onssen.utils.tester(args) tester.eval()
def main(): parser = argparse.ArgumentParser(description='Parse the config path') parser.add_argument( "-c", "--config", dest="path", help= 'The path to the config file. e.g. python run.py --config onfig.json') config = parser.parse_args() with open(config.path) as f: args = json.load(f) args = AttrDict(args) device = torch.device(args.device) args.model = nn.deep_clustering(**(args['model_options'])) args.model.to(device) args.train_loader = data.wsj0_2mix_dataloader(args.model_name, args.feature_options, 'tr', device) args.valid_loader = data.wsj0_2mix_dataloader(args.model_name, args.feature_options, 'cv', device) args.test_loader = data.wsj0_2mix_dataloader(args.model_name, args.feature_options, 'tt', device) args.optimizer = utils.build_optimizer(args.model.parameters(), args.optimizer_options) args.loss_fn = loss.loss_dc trainer = utils.trainer(args) trainer.run() tester = tester_dc(args) tester.eval()
def main(): config_path = './config.json' with open(config_path) as f: args = json.load(f) args = AttrDict(args) device = torch.device(args.device) args.device = device args.model = nn.ConvTasNet(**args["model_options"]) args.model.to(device) args.train_loader = data.wsj0_2mix_dataloader(args.model_name, args.feature_options, 'tr', device) args.valid_loader = data.wsj0_2mix_dataloader(args.model_name, args.feature_options, 'cv', device) args.test_loader = data.wsj0_2mix_dataloader(args.model_name, args.feature_options, 'tt', device) args.optimizer = utils.build_optimizer(args.model.parameters(), args.optimizer_options) args.loss_fn = loss.si_snr_loss trainer = utils.trainer(args) trainer.run() tester = tester_tasnet(args) tester.eval()
def main(): config_path = './config.json' with open(config_path) as f: args = json.load(f) args = AttrDict(args) device = torch.device(args.device) args.model = nn.chimera(**(args['model_options'])) args.model.to(device) args.train_loader = data.wsj0_2mix_dataloader(args.model_name, args.feature_options, 'tr', device) args.valid_loader = data.wsj0_2mix_dataloader(args.model_name, args.feature_options, 'cv', device) args.test_loader = data.wsj0_2mix_dataloader(args.model_name, args.feature_options, 'tt', device) args.optimizer = utils.build_optimizer(args.model.parameters(), args.optimizer_options) args.loss_fn = loss.loss_chimera_msa trainer = utils.trainer(args) trainer.run() tester = tester_chimera(args) tester.eval()